Agenda for today

• Course overview: Why learn R?
• Class logistics
• Variable types
• Vectors, matrices and lists

The big data explosion

• McKinsey Global Institute (Dec 2016): Volume of data doubles every 3 years
• US alone has 2 ZB = 2,000,000,000,000 GB of data!
• We need computing tools to make sense of data

What is R?

• S programming language: Developed in the mid-1970s in Bell Labs
  • An interactive approach to statistical computing: “… to turn ideas into software, quickly and faithfully”
  • Specifically designed for data analysis and graphics
• R is a version of S, with some ideas inspired by Scheme
• Created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand
• Conceptualized in 1992, initial version released in 1995, 1st stable version in 2000
• Currently developed by the R Development Core Team

Ross Ihaka & Rob Gentleman

Why learn R?

Reason #1: R was specifically designed for statistics and data analysis.

• Many features to aid data analysis which other programming languages don’t have
  
• Interactive, well-suited for exploratory data analysis and rapid prototyping
  
• Good graphical capabilities

Map of US obesity rates (Source: stackoverflow.com)

Why learn R?

Reason #2: It’s popular!

“What software have you used for Analytics, Data Mining, Data Science and/or Machine Learning projects in the past 12 months?”

(Source: KDnuggets)

Why learn R?

Reason #3: It’s easy to get started with R.

• Open source: free!
• Several online tutorials
• Active community developing “packages”
  • Shared for free on The Comprehensive R Network (CRAN)
  • As of 2 Oct 2017: 11,537 packages hosted on CRAN
• Several online resources available

The R Journal

Bi-annual open-access journal: Features short to medium length articles covering topics of interest to R users and developers

R-bloggers

Stack Overflow

Q&A site for programmers

R-exercises

Website with both tutorials and exercises

The challenge of learning R

• “By statisticians for statisticians”: very different from other programming languages
• Language evolves over time: have to keep up
• Many different ways to do the same thing

df[df$mpg > 30,]
with(df, df[mpg > 30,])
subset(df, mpg > 30)
filter(df, mpg > 30)
df %>% filter(mpg > 30)

Course objectives

By the end of this course, students will be able to:

• Navigate the R ecosystem at a basic level (RStudio, CRAN, R markdown, documentation)
  
• Open, manipulate, plot and save data
  
• Perform simple simulations, data modeling and statistical analyses in R

Tentative overview of the course

• Week 1: Introduction to RStudio, basic objects in R
• Week 2: Plotting and transforming data
• Week 3: Importing, cleaning and publishing data
• Week 4: Applications (data modeling, text mining)

This course is not…

• A statistics course, but…
• A computer science course, but…
• A “theory of R” course, but…

Class logistics

Class logistics

• Bring your laptop to class!
• Course website: https://canvas.stanford.edu/courses/72884
• Office hours: Fridays 10am - 12pm, Sequoia Hall Room 207
• Recommended text: R for Data Science by Garrett Grolemund and Hadley Wickham
• Final project proposal (due Oct 20): 20%
• Final project (due Nov 3): 80%

What is a variable?

• An “envelope” or “bucket” where information can be stored and referenced
• An abstraction that allows us to deal with the underlying structure and relatioships
• Each “bucket” has a name on the outside, contains information inside
  • Variable name: name of the bucket
  • Variable value: information on the inside
• Variable assignment: process of putting information inside a bucket

x <- 3

Variable types

• Every programming language has the notion of data types
• Common types
  • integer (…, -1, 0, 1, 2, …)
  • double (real numbers),
  • character (“a”, “R is cool”, “the quick brown fox…”, “1”)
  • Boolean (TRUE or FALSE)
• The object’s type determines what operations can be done with it
  • E.g. You can add 2 numbers, but can you add a number and a letter?

Confusion: 123 vs. “123”

How to differentiate between numeric variables and character variables which consist of digits?

• Should I treat it as a number like I do in math?
  • e.g. Does it make sense to add 2 of them together?
  • e.g. Does it make sense to compare their relative sizes?
• Numeric variables: counts, age, …
• Character variables: Phone numbers, ZIP codes, …

Let’s try R!

• Future classes: RStudio (installation instructions on Canvas)
• For today: http://www.r-fiddle.org/